The interest in the area of affective computing has grown dramatically in recent years. This interdisciplinary field, which spans computer science, cognitive science, and psychology, involves the study and development of systems that can recognize, interpret, process, and even simulate expression of human emotions. Presently, most affective computing systems are still research-grade endeavors that are typically not robust enough to handle the demands of real world applications. That being said, the continuing increase in computing power coupled with the miniaturization and proliferation of sensors and mobile devices, is making widespread adoption of affective computing systems in real world, day-to-day situations, closer than ever.
One useful capability for many affective computing applications is the ability to foretell a user's response to stimuli. Having this ability can enable the applications to offer a better user experience. While currently there are some systems that learn to predict a user's response to stimuli, they are mostly inadequate when it comes to real world applications. Existing systems are usually trained on data collected in a controlled environment. In these laboratory-like settings, a small number of short experiments are conducted (typically less than an hour long), in which users' responses are measured to a set of pre-selected stimuli, such as pictures, video scenes, or music. One drawback of the laboratory-collected data is that it is acquired over a short period of time, and the user is typically exposed to one stimulus at a time. However, in reality, a user's reaction to stimuli may vary dramatically depending on the situation the user is in, making the laboratory-collected data less useful. For example, a user's response while driving in busy traffic might be quite different from the user's response when relaxing at home, even if exposed to the very similar stimuli in both situations. Furthermore, with short experiments, a user's reaction can only be measured to a small number of stimuli, which is often inadequate for creating an affective computing system for real world applications that may have to model the effect of a wide range of stimuli from multiple sources. For example, a user may be exposed to multiple stimuli coming from digital media, such as video images and sound, while at the same time, the user may be exposed to physiological sensation stimuli originating from the massage chair the user is sitting in. The simultaneous exposure to multiple stimuli might have non-linear effects on the user, so for instance, the user's response might not add-up to the response predicted for each individual stand-alone stimulus. With the many challenges and complications involved in the real world domain, a system designed to predict a user's response to stimuli in real world scenarios may need to take into account the added complexity intrinsic to this domain in order to achieve optimal results.